Guillermo Lorenzo


Research




Patient-specific, imaging-based forecasting of prostate cancer growth


This research aims at integrating standard clinical and imaging data from individual patients into mathematical models to enable the prediction of tumor growth using computer simulations




Personalized prediction of PSA dynamics after external radiotherapy of prostate cancer


Exploring the biophysical mechanisms underlying PSA dynamics after external radiotherapy to define new biomarkers for the early identification of relapse




Optimal control of therapeutic regimens for advanced prostate cancer


This work aims at finding optimal combinations of cytotoxic and antiangiogenic therapies to treat advanced prostatic tumors by combining mathematical analysis and computer simulations




Integrating multiscale data and mechanistic models to predict breast cancer response to neoadjuvant therapies


Personalized prediction of breast cancer response to neoadjuvant therapies by using biophysical models parameterized with patient-specific imaging data and constrained by comprehensive pharmacodynamic experimental data




Data-driven mechanistic models to forecast COVID-19 outbreaks


Constructing mathematical models to understand and predict the dynamics of COVID-19 infectious spread based on longitudinal epidemiological data series